intrusion detection

This work intend to identify characteristics in network traffic that are able to distinguish the normal network behavior from denial of service attacks. One way to classify anomalous traffic is the data analysis of the packets header. This dataset contains labeled examples of normal traffic (23.088 instances), TCP Flood attacks (14.988 instances), UDP Flood (6.894 instances), HTTP Flood (347 instances) and HTTP Slow (183 instances) distributed in 73 numeric variables.

  • Security
  • Last Updated On: 
    Sat, 01/12/2019 - 13:18

    This dataset is benchmark dataset we use in our research for Intrusion Detection System.

  • Security
  • Last Updated On: 
    Sat, 12/29/2018 - 18:44

    Efficient intrusion detection and analysis of the security landscape in big data environments present challenge for today's users. Intrusion behavior can be described by provenance graphs that record the dependency relationships between intrusion processes and the infected files. Existing intrusion detection methods typically analyze and identify the anomaly either in a single provenance path or the whole provenance graph, neither of which can achieve the benefit on both detection accuracy and detection time.

  • Security
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Yulai Xie